



"""simple docstring"""





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__lowerCAmelCase							:       Optional[int]          =       logging.get_logger(__name__)



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							['''pixel_values''']





		def __init__(       self				,     _lowercase = True				,     _lowercase = 1 / 2_5_5				,     _lowercase = True				,     _lowercase = 8				,     **_lowercase				,     )			->    None:







								'''simple docstring'''

								super().__init__(**_lowercase )

								snake_case_ :			Any            =      do_rescale
								snake_case_ :			List[str]            =      rescale_factor
								snake_case_ :			Optional[int]            =      do_pad
								snake_case_ :			List[str]            =      pad_size





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase )			->    np.ndarray:







								'''simple docstring'''

								return rescale(_lowercase				,     scale=_lowercase				,     data_format=_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None )			->    List[Any]:







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			Any            =      get_image_size(_lowercase )
								snake_case_ :			List[str]            =      (old_height // size + 1) * size - old_height
								snake_case_ :			int            =      (old_width // size + 1) * size - old_width

								return pad(_lowercase				,     ((0, pad_height), (0, pad_width))				,     mode="""symmetric"""				,     data_format=_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = ChannelDimension.FIRST				,     **_lowercase				,     )			->    Any:







								'''simple docstring'''

								snake_case_ :			str            =      do_rescale if do_rescale is not None else self.do_rescale
								snake_case_ :			Optional[Any]            =      rescale_factor if rescale_factor is not None else self.rescale_factor
								snake_case_ :			Union[str, Any]            =      do_pad if do_pad is not None else self.do_pad
								snake_case_ :			int            =      pad_size if pad_size is not None else self.pad_size

								snake_case_ :			Optional[Any]            =      make_list_of_images(_lowercase )

								if not valid_images(_lowercase ):
														raise ValueError(
														    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
														    """torch.Tensor, tf.Tensor or jax.ndarray.""" )

								if do_rescale and rescale_factor is None:
														raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

								# All transformations expect numpy arrays.
								snake_case_ :			Optional[int]            =      [to_numpy_array(_lowercase ) for image in images]

								if do_rescale:
														snake_case_ :			Any            =      [self.rescale(image=_lowercase				,     scale=_lowercase ) for image in images]

								if do_pad:
														snake_case_ :			Union[str, Any]            =      [self.pad(_lowercase				,     size=_lowercase ) for image in images]

								snake_case_ :			int            =      [to_channel_dimension_format(_lowercase				,     _lowercase ) for image in images]

								snake_case_ :			str            =      {"""pixel_values""": images}
								return BatchFeature(data=_lowercase				,     tensor_type=_lowercase )








'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



